def main(args):
parser = argparse.ArgumentParser(
description="Train a RL net to play in an OpenAI Gym environment.")
parser.add_argument("-p",
"--parameters",
help="Path to JSON parameters file.")
parser.add_argument(
"-s",
"--score-bar",
help="Bar for averaged tests scores.",
type=float,
default=None,
)
parser.add_argument(
"-g",
"--gpu_id",
help="If set, will use GPU with specified ID. Otherwise will use CPU.",
default=USE_CPU,
)
parser.add_argument(
"-l",
"--log_level",
help="If set, use logging level specified (debug, info, warning, error, "
"critical). Else defaults to info.",
default="info",
)
parser.add_argument(
"-f",
"--file_path",
help="If set, save all collected samples as an RLDataset to this file.",
default=None,
)
parser.add_argument(
"-e",
"--start_saving_from_episode",
type=int,
help=
"If file_path is set, start saving episodes from this episode num.",
default=0,
)
parser.add_argument(
"-r",
"--results_file_path",
help="If set, save evaluation results to file.",
type=str,
default=None,
)
args = parser.parse_args(args)
if args.log_level not in ("debug", "info", "warning", "error", "critical"):
raise Exception("Logging level {} not valid level.".format(
args.log_level))
else:
logger.setLevel(getattr(logging, args.log_level.upper()))
with open(args.parameters, "r") as f:
params = json.load(f)
dataset = RLDataset(args.file_path) if args.file_path else None
reward_history, timestep_history, trainer, predictor = run_gym(
params, args.score_bar, args.gpu_id, dataset,
args.start_saving_from_episode)
if dataset:
dataset.save()
if args.results_file_path:
write_lists_to_csv(args.results_file_path, reward_history,
timestep_history)
return reward_history
def main(args):
parser = argparse.ArgumentParser(
description="Train a RL net to play in an OpenAI Gym environment."
)
parser.add_argument("-p", "--parameters", help="Path to JSON parameters file.")
parser.add_argument(
"-s",
"--score-bar",
help="Bar for averaged tests scores.",
type=float,
default=None,
)
parser.add_argument(
"-g",
"--gpu_id",
help="If set, will use GPU with specified ID. Otherwise will use CPU.",
default=USE_CPU,
)
parser.add_argument(
"-l",
"--log_level",
help="If set, use logging level specified (debug, info, warning, error, "
"critical). Else defaults to info.",
default="info",
)
parser.add_argument(
"-f",
"--file_path",
help="If set, save all collected samples as an RLDataset to this file.",
default=None,
)
parser.add_argument(
"-e",
"--start_saving_from_episode",
type=int,
help="If file_path is set, start saving episodes from this episode num.",
default=0,
)
parser.add_argument(
"-r",
"--results_file_path",
help="If set, save evaluation results to file.",
type=str,
default=None,
)
args = parser.parse_args(args)
if args.log_level not in ("debug", "info", "warning", "error", "critical"):
raise Exception("Logging level {} not valid level.".format(args.log_level))
else:
logger.setLevel(getattr(logging, args.log_level.upper()))
with open(args.parameters, "r") as f:
params = json.load(f)
dataset = RLDataset(args.file_path) if args.file_path else None
reward_history, timestep_history, trainer, predictor = run_gym(
params, args.score_bar, args.gpu_id, dataset, args.start_saving_from_episode
)
if dataset:
dataset.save()
if args.results_file_path:
write_lists_to_csv(args.results_file_path, reward_history, timestep_history)
return reward_history